China's surface urban heat island drivers and its spatial heterogeneity

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	China's surface urban heat island drivers and its spatial heterogeneity
	Niu, Lu 1; Zhang, Zheng-Feng 1; Peng, Zhong 2,3; Jiang, Ya-Zhen 2,3; Liu, Meng 4; Zhou, Xiao-Min 5; Tang, Rong-Lin 2,3
刊名	Zhongguo Huanjing Kexue/China Environmental Science
	2022-02-20
卷号	42 期号:2 页码:945-953
关键词	Atmospheric temperature Land use Landforms Radiometers Regression analysis Remote sensing Spatial distribution Vegetation Driver Geographically weighted regression Geographically Weighted Regression modelling Moderate res-olution imaging spectrometers Spatial heterogeneity Surface urban heat islands Thermal infrared remote sensing Urban environments Urban heat island Urban heat island intensities
ISSN号	1000-6923
英文摘要	Based on satellite remote sensing data acquired through Moderate Resolution Imaging Spectrometer (MODIS), not only was the annual mean surface urban heat island intensity of 284prefecture-level cities in 2018 figured out, but spatial distribution patterns and spatial agglomeration models of surface urban heat islands in China were analyzed. Combining multivariate remote sensing data, meteorological data and socioeconomic statistics, a geographically weighted regression model was utilized to analyze spatial heterogeneity in main drivers for surface urban heat island intensity during daytime and nighttime. As demonstrated by relevant results, an obvious spatial autocorrelation existed in spatial distribution of China's surface urban heat island intensity. Compared with the traditional global ordinary least squares (OLS) model, interpretation of the drivers was significantly improved according to the geographically weighted regression model. Moreover, determination coefficients for daytime and nighttime increased from 0.651 and 0.189 in the OLS model to 0.876 and 0.659 respectively. In addition, both the residual sum of squares and the Akaike information criterion were calculated to be lower by the geographically weighted regression model. In terms of the drivers, vegetation placed a significantly negative influence on surface urban heat island intensity during the daytime, while structural differences were proved to exist in directions of influence that was applied by other factors along with geographic position changes. On the whole, surface urban heat island intensity was most significantly affected by differences in urban and rural vegetation in daytime; but at night, it was susceptible to socio-economic factors. © 2022, Editorial Board of China Environmental Science. All right reserved.
语种	中文
出版者	Chinese Society for Environmental Sciences
内容类型	期刊论文
源URL	[http://ir.lut.edu.cn/handle/2XXMBERH/157842]
专题	兰州理工大学
作者单位	1.School of Public Administration and Policy, Renmin University of China, Beijing; 100872, China; 2.State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing; 100101, China; 3.University of Chinese Academy of Sciences, Beijing; 100049, China; 4.Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing; 100081, China; 5.School of Civil Engineering, Lanzhou University of Technology, Lanzhou; 730050, China
推荐引用方式 GB/T 7714	Niu, Lu,Zhang, Zheng-Feng,Peng, Zhong,et al. China's surface urban heat island drivers and its spatial heterogeneity[J]. Zhongguo Huanjing Kexue/China Environmental Science,2022,42(2):945-953.
APA	Niu, Lu.,Zhang, Zheng-Feng.,Peng, Zhong.,Jiang, Ya-Zhen.,Liu, Meng.,...&Tang, Rong-Lin.(2022).China's surface urban heat island drivers and its spatial heterogeneity.Zhongguo Huanjing Kexue/China Environmental Science,42(2),945-953.
MLA	Niu, Lu,et al."China's surface urban heat island drivers and its spatial heterogeneity".Zhongguo Huanjing Kexue/China Environmental Science 42.2(2022):945-953.